T = Team, O = Opponent
\(eFG\% = \frac{ (2PM)_T + 1.5 \times (3PM)_T }{ (2PA)_T + (3PA)_T}\)
\(TO = \frac{TOV_T}{POSS_T}\)
\(REB\% = \frac{OREB_T}{OREB_T + DREB_O}\)
\(FT\) Rate \(= \frac{FTM_T}{(2PA)_T + (3PA)_T}\)
The Four Factors by Kubatko, J., Oliver, D., Pelton, K., and Rosenbaum, D. T. (2007).
Round One: 1
Round Two: 2
Sweet Sixteen: 4
Elite Eight: 8
Final Four: 16
Championship: 32
A perfect bracket gets a score of 192.
This could be as simple as using AP rankings, or you could develop your own metric. You can evaluate your metric based on how it performs on past tournaments.
We can predict the probability of a team winning a certain March Madness game.
Strength of Schedule, Performance in Recent Games, Performance in Recent Seasons, Injuries/Suspensions, Location, Player Matchups, Offensive/Defensive Tendencies
Depending on choice of model, it may be possible that the team most likely to advance at one stage may be less likely to advance at a future stage.
We can predict how many points a team will score in a March Madness game.
Regression works for continuous variables that have a support of \((-\infty, \infty)\), so we must use a link function to map the counts to real variables.
For regression, let \(Y\) be the number of points scored by the team of interest, and let \(x_j\) be the \(j\)th predictor out of \(n\).
Then \(\log(E(Y | x)) = \theta_0 + \theta_1x_1 + \theta_2x_2 + ... + \theta_nx_n\)
For each team in a game, we can draw from Pois\((\lambda)\), where \(\lambda\) is the predicted response from our regression for that team’s points. The team that scores more points advances. We can simulate a tournament as many times as we want. Then we can get an idea of how likely a team is to make it to a certain round. Note that the most likely bracket may not coincide with the most likely winner.
Pick the teams you think will win, or the teams you personally want to win. It worked great for Florida students! Just make sure you don’t pick too few or too many upsets.
NCAA Average Number of Upsets: